View in EDS

A Contextually Relevant Framework for Integrating Robotics in Secondary Chemistry Teaching: The Case for Stoichiometry and Titration.

Saved in:
Bibliographic Details
Title: A Contextually Relevant Framework for Integrating Robotics in Secondary Chemistry Teaching: The Case for Stoichiometry and Titration.
Authors: Mhlongo, Thabo1 (AUTHOR) t.man907@hotmail.com, Kriek, Jeanne1 (AUTHOR), Gouws, Patricia M1 (AUTHOR)
Source: African Journal of Research in Mathematics, Science & Technology Education. Apr2026, Vol. 30 Issue 1, p58-76. 19p.
Subject Terms: *Inquiry-based learning, *STEM education, *Teaching models, *Educational technology, *Constructivism (Education), *Chemistry teachers, Stoichiometry, Volumetric analysis
Abstract: This study develops a contextually relevant robotic concept-based framework (RCF) for integrating robotics into secondary chemistry teaching, illustrated through the cases of stoichiometry and titration. The framework is designed for resource-constrained South African classrooms where limited laboratory access restricts practical experimentation and learner engagement. A systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines identified 17 peer-reviewed studies drawn from Scopus and Web of Science informing the construction of the RCF. Four interlinked dimensions were identified: pedagogical design, technological infrastructure, teacher development and support, and contextual adaptability. The framework aligns with constructivist and inquiry-based learning principles by embedding robotics into both deductive and inductive learning pathways. These pathways allow learners to interact with and visualise abstract chemical processes through hands-on and virtual experimentation. The RCF promotes conceptual understanding, learner motivation, and engagement, while developing computational and problem-solving skills. Illustrative lesson designs and an Arduino-based titration model demonstrate its adaptability across diverse classroom contexts. The framework provides a scalable, evidence-based approach for enhancing chemistry instruction through robotics, bridging the gap between theoretical abstraction and experiential learning in under-resourced STEM environments. [ABSTRACT FROM AUTHOR]
Copyright of African Journal of Research in Mathematics, Science & Technology Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
Description
Abstract:This study develops a contextually relevant robotic concept-based framework (RCF) for integrating robotics into secondary chemistry teaching, illustrated through the cases of stoichiometry and titration. The framework is designed for resource-constrained South African classrooms where limited laboratory access restricts practical experimentation and learner engagement. A systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines identified 17 peer-reviewed studies drawn from Scopus and Web of Science informing the construction of the RCF. Four interlinked dimensions were identified: pedagogical design, technological infrastructure, teacher development and support, and contextual adaptability. The framework aligns with constructivist and inquiry-based learning principles by embedding robotics into both deductive and inductive learning pathways. These pathways allow learners to interact with and visualise abstract chemical processes through hands-on and virtual experimentation. The RCF promotes conceptual understanding, learner motivation, and engagement, while developing computational and problem-solving skills. Illustrative lesson designs and an Arduino-based titration model demonstrate its adaptability across diverse classroom contexts. The framework provides a scalable, evidence-based approach for enhancing chemistry instruction through robotics, bridging the gap between theoretical abstraction and experiential learning in under-resourced STEM environments. [ABSTRACT FROM AUTHOR]
ISSN:18117295
DOI:10.1080/18117295.2025.2604838